Projecting survey based estimates of 2022 onto small areas

Author
Affiliation

Honorary Professor, University of Sydney

Published

12:25PM 3 May 2023

1 Predicting vote with Census data on AES

How well can we predict vote in the AES data with predictors that are available on the CURF? This includes the SA4-level Census data predictors.

Brier score: 0.2467291

Confusion matrix: for each actual outcome (row), distribution of predictions assigned with max prob:

Coalition Greens Independent Labor Other
Coalition 53.8 3.4 2.4 40.1 0.3
Greens 27.9 19.4 4.0 48.5 0.1
Independent 32.8 7.3 15.1 44.9 0.0
Labor 38.1 7.0 2.3 52.4 0.2
Other 51.5 5.0 2.6 39.9 1.0

1.1 Performance with modal missing data imputation

Brier score: 0.2454336

Confusion matrix: actual (rows) by predicted/max-prob (cols):

Coalition Greens Independent Labor Other
Coalition 53.5 4.1 2.2 40.0 0.2
Greens 29.8 19.2 4.0 46.7 0.3
Independent 31.7 5.9 16.1 46.2 0.0
Labor 38.1 8.2 1.9 51.6 0.2
Other 54.3 5.2 3.4 35.3 1.7

2 Inference for populations

2.1 Vote marginal for the CURF data from imputed data

2022 House vote
Coalition 19.9
Greens 20.0
Independent 20.0
Labor 20.1
Other 20.0

2.2 Predict onto CURF from model fit to complete AES data

2022 House vote MaxProb SumProbs Actual
Coalition 55.1 46.7 35.7
Greens 0.5 6.8 12.2
Independent 1.0 2.4 5.3
Labor 43.3 43.0 32.6

Variable importance:

rf variable importance

  only 20 most important variables shown (out of 28)

                                  Overall
RELP_collapsed                     100.00
AGE_continuous                      76.35
ANCP_collapse                       67.60
INCP_continuous                     52.93
MSTP                                44.60
UNI                                 37.24
Median_tot_hhd_inc_weekly_G02       36.17
Median_tot_fam_inc_weekly_G02       35.96
PC3                                 34.65
Median_mortgage_repay_monthly_G02   34.11
PC5                                 33.70
PC1                                 33.48
PC4                                 33.36
Median_tot_prsnl_inc_weekly_G02     33.34
PC6                                 32.91
PC9                                 32.72
Median_rent_weekly_G02              32.64
PC2                                 32.18
PC7                                 31.93
PC10                                31.89

3 Predictions for geographies

Combine predictions with other covariates on curf_complete

3.1 AREAENUM

3.2 Electoral divisions

3.3 Demographic imbalance in CED extracts from CURF

3.4 Rake CED extracts on demographics

Variation in weights:

Division s min max
Bendigo 2.5780416 0.0000086 30.859540
Sydney 1.7987436 0.0009711 12.199306
Casey 1.7944043 0.0002709 12.687224
New England 1.6482850 0.0000509 17.086233
Flinders 1.6333033 0.0000658 19.850798
Mallee 1.5726479 0.0130783 14.372747
Moreton 1.5478647 0.0151816 11.127717
Boothby 1.4428878 0.0053361 14.322665
Bennelong 1.4376156 0.0006818 13.620257
Wannon 1.4066912 0.0143034 13.658697
Makin 1.4027926 0.0000106 14.575613
Oxley 1.3967175 0.0133082 15.856979
Durack 1.3734926 0.0123059 10.704866
Parkes 1.3263571 0.0074893 15.274519
Barker 1.3234352 0.0000471 9.226240
Menzies 1.3208845 0.0095473 9.279756
Fowler 1.2885169 0.0035197 13.186444
Barton 1.2850943 0.0040512 9.874167
Dickson 1.2760560 0.0067525 12.076173
Grey 1.2547192 0.0033467 14.347527
Macquarie 1.2526396 0.0103729 12.017586
Wentworth 1.2509479 0.0128664 9.629454
Blair 1.2316744 0.0066233 8.565736
Werriwa 1.2301238 0.0270912 13.247889
Nicholls 1.1973524 0.0167508 8.376280
McEwen 1.1939602 0.0166799 11.065251
Indi 1.1877036 0.0031423 8.382811
Brisbane 1.1436707 0.0156068 6.731995
Pearce 1.1387601 0.0001674 6.936223
Forde 1.1372234 0.0496546 9.618007
Hume 1.1190159 0.0000241 7.933744
Calare 1.0950057 0.0100608 8.436689
Griffith 1.0902510 0.0032716 7.620977
Greenway 1.0811338 0.0104492 8.453713
Whitlam 1.0467476 0.0582190 8.933270
Cook 1.0419148 0.0364373 13.953604
Isaacs 1.0404038 0.0515471 8.528984
Watson 1.0365405 0.0069620 7.264573
Bonner 1.0219053 0.0007176 7.441562
Deakin 1.0186199 0.0101797 8.220599
Gorton 0.9975589 0.0150294 7.033615
McMahon 0.9966654 0.0090440 12.839455
Paterson 0.9903204 0.0169473 7.453546
North Sydney 0.9830184 0.0185560 9.034169
Mitchell 0.9817589 0.0276072 14.058285
Groom 0.9693507 0.0232321 7.672426
Riverina 0.9450200 0.0000108 5.985934
Hawke 0.9406561 0.0061943 7.606924
Maranoa 0.9358403 0.0320753 6.922041
Curtin 0.9332948 0.0665137 13.835585
Farrer 0.9262207 0.0413802 11.565611
Kennedy 0.9247250 0.0304334 6.378256
Hughes 0.9232049 0.0578815 10.750154
Rankin 0.9084038 0.0562276 8.763248
Adelaide 0.8942120 0.0403152 8.575549
Cooper 0.8930565 0.0147946 7.515505
Hasluck 0.8903487 0.0243475 4.935279
Sturt 0.8860890 0.0516610 12.485082
Flynn 0.8845663 0.0626193 11.311120
Macnamara 0.8596823 0.0932197 12.305099
Perth 0.8448319 0.0685515 6.721007
Gellibrand 0.8408229 0.1001703 11.718316
Berowra 0.8393769 0.0115527 8.051217
Tangney 0.8261073 0.0653509 11.432890
Macarthur 0.8230161 0.0593629 8.172548
Reid 0.8225637 0.0335505 7.261453
Petrie 0.7908424 0.0546750 9.178553
Wright 0.7848379 0.0463837 7.367668
Hotham 0.7822530 0.0343599 7.895685
Jagajaga 0.7810443 0.0626807 8.612915
Shortland 0.7780678 0.0612036 6.363784
Cowan 0.7519233 0.0573316 7.946864
Gilmore 0.7496870 0.0460703 5.371107
Chisholm 0.7414970 0.0800713 9.205027
Capricornia 0.7389866 0.0773710 7.933174
La Trobe 0.7386513 0.0298348 5.699612
Fraser 0.7340315 0.0648693 7.765142
Lingiari 0.7265968 0.1286400 6.324772
Canning 0.7243790 0.0300809 8.691773
Higgins 0.7210573 0.0618029 8.158822
Canberra 0.7094029 0.1062587 7.947039
Robertson 0.7019808 0.0949866 7.162520
Scullin 0.7005307 0.1204179 6.758612
Lyne 0.7004547 0.0399576 5.005509
Calwell 0.6991125 0.1412705 5.234313
Wide Bay 0.6971233 0.0398779 6.112446
Clark 0.6962159 0.1053896 6.588164
Blaxland 0.6912721 0.0588989 4.951604
Eden-Monaro 0.6874472 0.1709286 6.638256
Richmond 0.6785678 0.0490977 7.124612
Kingsford Smith 0.6762163 0.1219288 5.377459
Mayo 0.6670176 0.0130771 5.563939
Bradfield 0.6619736 0.1106240 6.581924
Newcastle 0.6577251 0.1008045 6.896389
Corangamite 0.6542218 0.1132389 9.821724
Grayndler 0.6406635 0.0515023 4.828771
Solomon 0.6365387 0.0933097 4.986568
Lalor 0.6342810 0.1037005 5.545238
Cunningham 0.6319962 0.1236790 6.662259
Leichhardt 0.6312873 0.1206082 5.560562
Hinkler 0.6215269 0.0624515 5.085246
Moore 0.6209194 0.1144009 6.467165
Banks 0.6206930 0.1942334 5.672632
Chifley 0.6197328 0.0874513 4.782245
Holt 0.6164560 0.0443667 5.601250
Corio 0.6055126 0.1588281 6.255713
Franklin 0.5878195 0.1062300 6.417149
Melbourne 0.5733922 0.1660150 4.116145
Kooyong 0.5669183 0.1099543 5.828383
Moncrieff 0.5651720 0.1252925 5.681130
Swan 0.5607994 0.1452317 6.905182
Lindsay 0.5586542 0.1406972 4.154971
Lilley 0.5583978 0.1032221 5.358251
Parramatta 0.5563628 0.1227808 5.001099
Aston 0.5513989 0.1706816 5.040813
Monash 0.5465856 0.0877061 5.653150
Herbert 0.5452790 0.0868871 4.582953
Maribyrnong 0.5449552 0.1232340 5.488512
Forrest 0.5447834 0.0864451 6.175120
Dobell 0.5437386 0.1396497 4.395371
Dunkley 0.5435595 0.1713133 4.767757
Warringah 0.5434095 0.0987807 4.238104
Goldstein 0.5418253 0.1301611 5.525831
McPherson 0.5413583 0.1083691 5.434242
Longman 0.5353452 0.0768650 3.733408
Hunter 0.5295370 0.1100505 4.743364
Fremantle 0.5236029 0.1160798 4.401008
Bruce 0.5206726 0.1335883 5.451724
Ballarat 0.5202467 0.1180586 6.364325
O'Connor 0.5189334 0.0547496 4.075871
Cowper 0.5187695 0.0960574 4.770949
Dawson 0.5161077 0.0841633 6.524573
Gippsland 0.5136464 0.0846250 4.894813
Bowman 0.5133425 0.1172712 4.000088
Fadden 0.5016992 0.1358906 4.412945
Fairfax 0.5007181 0.0963938 3.927981
Fisher 0.4953689 0.1093310 3.648678
Braddon 0.4867593 0.0457050 5.209183
Wills 0.4777914 0.1836633 4.666492
Spence 0.4726178 0.1110867 3.321669
Brand 0.4682428 0.1037821 4.441956
Page 0.4636118 0.0689725 3.884404
Ryan 0.4635004 0.1900109 4.107098
Mackellar 0.4334901 0.0751803 2.521836
Kingston 0.4312959 0.1926938 3.132224
Burt 0.4157660 0.1990193 3.329439
Bass 0.4157223 0.2095118 3.090007
Bean 0.4075736 0.1933086 3.323672
Fenner 0.4057896 0.1337472 2.968078
Lyons 0.4017169 0.0543050 3.083122
Hindmarsh 0.3964710 0.1443560 3.695762

Weighted estimates of vote:

Warning in left_join(yhat %>% select(.id, outcome, p), ced_raked %>% unnest(d), : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 223191 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.